Can we agree on an ecological classification of subterranean animals?

That the ties between any obligate subterranean species and the hypogean environment depend on the interplay of a species' own physiological characteristics and all of the ecological characteristics of the adjacent epigean habitat(s), including biotic factors, has been emphasized. The reasons why troglomorphy cannot be included within criteria for classifying cave dwellers have been demonstrated. After a review of historic classifications, standardized definitions of the most widely used terms are proposed. The most easily recognized are four categories which are terminological approximations of the classic Schiner‐Racovitza terminology: (1) troglobiont is a species or population, strictly bound to a hypogean habitat; (2) eutroglophile is an essentially epigean species, but able to maintain a permanent subterranean population; (3) subtroglophile is inclined perpetually or temporarily to inhabit a subterranean habitat but is bound to the surface for some biological functions (e.g. feeding); (4) trogloxene is a species only occurring sporadically underground.     

Ultrastructural redescription of Chordodes moraisi (Carvalho, 1942) and Chordodes straviarskii Carvalho and Feio, 1950, and re‐interpretation of Chordodes gestri Camerano, 1904 and Pseudochordodes griffinii (Camerano, 1898) (Gordiida,Nematomorpha)

The Gordiida species Chordodes moraisi (Carvalho, ) and C. staviarskii Carvalho and Feio, are re‐described using scanning electron microscopy (SEM). Chordodes moraisi is characterized by four areole types, two of which correspond to tubercle areoles, the crown areoles appear as single structures and occur on both sides along the ventral midline. Chordodes staviarskii is characterized by a polymorphism of crowned areoles which appear as two types with distinct distribution patterns. Two species, Chordodes gestri Camerano, and Pseudochordodes griffinii (Camerano, ), are synonymized with Neochordodes occidentalis (Montgomery, 1898) and Chordodes peraccae (Camerano, 1894).     

Do caterpillars of Dryas iulia alcionea (Lepidoptera,Nymphalidae) show evidence of adaptive behaviour to avoid predation by ants?

Plants bearing extrafloral nectaries can attract wasps, bugs, and mainly ants. Ants can defend plants against herbivores by treating the plant as an extension of their own territory. We evaluated the efficiency of resting sites built by Dryas iulia alcionea larvae as protection against ant predation on their host plant Passiflora suberosa. The results showed that ants on P. suberosa have significant influence on the survival of D. iulia caterpillars. The use and construction of resting sites was recorded through the fourth larval stadium. A strong inverse relationship between the number of resting sites constructed per larva and the developmental stage of the larvae was observed. Additional results suggest that the efficacy of resting sites against cursorial predators decreases with larval development. The behaviour of constructing resting sites probably evolved by natural selection. It is likely that this behaviour was important for the ancestral lineage that gave origin to Heliconiinae genera Dryas, Dryadula and Phylaethria.     

A Meta‐learning Framework for Bankruptcy Prediction

The implication of corporate bankruptcy prediction is important to financial institutions when making lending decisions. In related studies, many bankruptcy prediction models have been developed based on some machine‐learning techniques. This paper presents a meta‐learning framework, which is composed of two‐level classifiers for bankruptcy prediction. The first‐level multiple classifiers perform the data reduction task by filtering out unrepresentative training data. Then, the outputs of the first‐level classifiers are utilized to create the second‐level single (meta) classifier. The experiments are based on five related datasets and the results show that the proposed meta‐learning framework provides higher prediction accuracy rates and lower type I/II errors when compared with the stacked generalization classifier and other three widely developed baselines, such as neural networks, decision trees, and logistic regression. Copyright © 2011 John Wiley & Sons, Ltd.     

Identification of asymmetric prediction intervals through causal forces

When causal forces are specified, the expected direction of the trend can be compared with the trend based on extrapolation. Series in which the expected trend conflicts with the extrapolated trend are called contrary series. We hypothesized that contrary series would have asymmetric forecast errors, with larger errors in the direction of the expected trend. Using annual series that contained minimal information about causality, we examined 671 contrary forecasts. As expected, most (81%) of the errors were in the direction of the causal forces. Also as expected, the asymmetries were more likely for longer forecast horizons; for six‐year‐ahead forecasts, 89% of the forecasts were in the expected direction. The asymmetries were often substantial. Contrary series should be flagged and treated separately when prediction intervals are estimated, perhaps by shifting the interval in the direction of the causal forces. Copyright © 2001 John Wiley & Sons, Ltd.     

Local to unity,long‐horizon forecasting thresholds for model selection in the AR(1)

This article introduces a novel framework for analysing long‐horizon forecasting of the near non‐stationary AR(1) model. Using the local to unity specification of the autoregressive parameter, I derive the asymptotic distributions of long‐horizon forecast errors both for the unrestricted AR(1), estimated using an ordinary least squares (OLS) regression, and for the random walk (RW). I then identify functions, relating local to unity ‘drift’ to forecast horizon, such that OLS and RW forecasts share the same expected square error. OLS forecasts are preferred on one side of these ‘forecasting thresholds’, while RW forecasts are preferred on the other. In addition to explaining the relative performance of forecasts from these two models, these thresholds prove useful in developing model selection criteria that help a forecaster reduce error. Copyright © 2004 John Wiley & Sons, Ltd.     

基于支持向量机的换档质量评价系统

车辆换档质量是自动档车辆最重要的性能之一,因此换档质量的评价一直是车辆性能研究的一个重点。以支持向量机理论为核心创建的换档质量评价系统,综合考虑了车辆换档的各种工况和影响因素,利用支持向量机良好的泛化能力,能够稳定的对换档质量进行动态综合的评价。     

基于K均值和支持向量机的燃料电池在线自适应故障诊断

基于K均值(K-means)和支持向量机(support vector machine, SVM)算法,提出了一种车用燃料电池系统(fuel cell system, FCS)在线自适应故障诊断方法.该方法通过不断获取系统最新单体电压,采用K-means算法改进传统的静态SVM分类器模型,对实时获取的信息进行聚类,实现分类器的在线自适应调节.采用已发表文献中的实验数据进行了相关的验证分析,结果表明,提出的方法能有效地在线调节故障分类器,实现FCS系统特性发生改变后的故障检测.     

基于二维离散小波的生成图像鉴别方法

针对传统基于神经网络的计算机生成图像鉴别方法中存在鉴别难度大和准确率低的问题, 提出一种采用基于小波变换的计算机生成图像鉴别方法. 首先在进行图像多维小波特征提取时, 通过一次分解二维离散小波变换提取图像小波特征, 根据图像小波特征进行n级小波分解提取图像多维小波特征向量; 然后通过三维变换域波去噪算法（BM3D）提取计算机生成图像噪声特征; 最后采用支持向量机(SVM)分类器对计算机生成图像进行鉴别, 通过SVM分类器对图像多维小波特征和噪声特征进行分类, 以解决两种特征融合形成线性不可分的高维特征问题, 从而实现计算机生成图像的准确鉴别. 实验结果表明, 该方法在鉴别计算机生成图像时具有更高的准确性和稳定性.